/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    NominalPrediction.java
 *    Copyright (C) 2002-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.evaluation;

import java.io.Serializable;

/**
 * Encapsulates an evaluatable nominal prediction: the predicted probability
 * distribution plus the actual class value.
 *
 * @author Len Trigg (len@reeltwo.com)
 * @version $Revision$
 */
public class NominalPrediction implements Prediction, Serializable {

    /**
     * Remove this if you change this class so that serialization would be affected.
     */
    static final long serialVersionUID = -8871333992740492788L;

    /** The predicted probabilities */
    private double[] m_Distribution;

    /** The actual class value */
    private double m_Actual = MISSING_VALUE;

    /** The predicted class value */
    private double m_Predicted = MISSING_VALUE;

    /** The weight assigned to this prediction */
    private double m_Weight = 1;

    /**
     * Creates the NominalPrediction object with a default weight of 1.0.
     *
     * @param actual       the actual value, or MISSING_VALUE.
     * @param distribution the predicted probability distribution. Use
     *                     NominalPrediction.makeDistribution() if you only know the
     *                     predicted value.
     */
    public NominalPrediction(double actual, double[] distribution) {

        this(actual, distribution, 1);
    }

    /**
     * Creates the NominalPrediction object.
     *
     * @param actual       the actual value, or MISSING_VALUE.
     * @param distribution the predicted probability distribution. Use
     *                     NominalPrediction.makeDistribution() if you only know the
     *                     predicted value.
     * @param weight       the weight assigned to the prediction.
     */
    public NominalPrediction(double actual, double[] distribution, double weight) {

        if (distribution == null) {
            throw new NullPointerException("Null distribution in NominalPrediction.");
        }
        m_Actual = actual;
        m_Distribution = distribution.clone();
        m_Weight = weight;
        updatePredicted();
    }

    /**
     * Gets the predicted probabilities
     * 
     * @return the predicted probabilities
     */
    public double[] distribution() {

        return m_Distribution;
    }

    /**
     * Gets the actual class value.
     *
     * @return the actual class value, or MISSING_VALUE if no prediction was made.
     */
    public double actual() {

        return m_Actual;
    }

    /**
     * Gets the predicted class value.
     *
     * @return the predicted class value, or MISSING_VALUE if no prediction was
     *         made.
     */
    public double predicted() {

        return m_Predicted;
    }

    /**
     * Gets the weight assigned to this prediction. This is typically the weight of
     * the test instance the prediction was made for.
     *
     * @return the weight assigned to this prediction.
     */
    public double weight() {

        return m_Weight;
    }

    /**
     * Calculates the prediction margin. This is defined as the difference between
     * the probability predicted for the actual class and the highest predicted
     * probability of the other classes.
     *
     * @return the margin for this prediction, or MISSING_VALUE if either the actual
     *         or predicted value is missing.
     */
    public double margin() {

        if ((m_Actual == MISSING_VALUE) || (m_Predicted == MISSING_VALUE)) {
            return MISSING_VALUE;
        }
        double probActual = m_Distribution[(int) m_Actual];
        double probNext = 0;
        for (int i = 0; i < m_Distribution.length; i++)
            if ((i != m_Actual) && (m_Distribution[i] > probNext))
                probNext = m_Distribution[i];

        return probActual - probNext;
    }

    /**
     * Convert a single prediction into a probability distribution with all zero
     * probabilities except the predicted value which has probability 1.0. If no
     * prediction was made, all probabilities are zero.
     *
     * @param predictedClass the index of the predicted class, or MISSING_VALUE if
     *                       no prediction was made.
     * @param numClasses     the number of possible classes for this nominal
     *                       prediction.
     * @return the probability distribution.
     */
    public static double[] makeDistribution(double predictedClass, int numClasses) {

        double[] dist = new double[numClasses];
        if (predictedClass == MISSING_VALUE) {
            return dist;
        }
        dist[(int) predictedClass] = 1.0;
        return dist;
    }

    /**
     * Creates a uniform probability distribution -- where each of the possible
     * classes is assigned equal probability.
     *
     * @param numClasses the number of possible classes for this nominal prediction.
     * @return the probability distribution.
     */
    public static double[] makeUniformDistribution(int numClasses) {

        double[] dist = new double[numClasses];
        for (int i = 0; i < numClasses; i++) {
            dist[i] = 1.0 / numClasses;
        }
        return dist;
    }

    /**
     * Determines the predicted class (doesn't detect multiple classifications). If
     * no prediction was made (i.e. all zero probababilities in the distribution),
     * m_Prediction is set to MISSING_VALUE.
     */
    private void updatePredicted() {

        int predictedClass = -1;
        double bestProb = 0.0;
        for (int i = 0; i < m_Distribution.length; i++) {
            if (m_Distribution[i] > bestProb) {
                predictedClass = i;
                bestProb = m_Distribution[i];
            }
        }

        if (predictedClass != -1) {
            m_Predicted = predictedClass;
        } else {
            m_Predicted = MISSING_VALUE;
        }
    }

    /**
     * Gets a human readable representation of this prediction.
     *
     * @return a human readable representation of this prediction.
     */
    public String toString() {

        StringBuffer sb = new StringBuffer();
        sb.append("NOM: ").append(actual()).append(" ").append(predicted());
        sb.append(' ').append(weight());
        double[] dist = distribution();
        for (int i = 0; i < dist.length; i++) {
            sb.append(' ').append(dist[i]);
        }
        return sb.toString();
    }

}
